[1]JI Xiaofei,QIN Ningli,LIU Yang.Research on multi-feature based multi-target recognition algorithm for optical remote sensing image[J].CAAI Transactions on Intelligent Systems,2016,11(5):655-662.[doi:10.11992/tis.201511011]
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Research on multi-feature based multi-target recognition algorithm for optical remote sensing image

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 5 Page number: 655-662 Column: 学术论文—机器感知与模式识别 Public date: 2016-11-01
Title:
Research on multi-feature based multi-target recognition algorithm for optical remote sensing image
Author(s):
JI Xiaofei1;  QIN Ningli2;  LIU Yang1
1. School of Automation, Shenyang Aerospace University, Shenyang 110136, China;
2. Beijing GuoDianTong Network Technology Co. Ltd, Beijing 100070, China
Keywords:
optical remote sensing image; multi-features decision level fusion; hierarchical BoF-SIFT feature; shape context feature; Hu moment invariants; support vector machine
CLC:
TP751.1
DOI:
10.11992/tis.201511011
Abstract:
A novel multi-feature decision level fusion recognition algorithm is proposed to solve the problem of poor levels of accuracy with single feature based multi-target classification of optical remote sensing images. Firstly, three kinds of features which can not only meet translation, rotation, and scale invariance are extracted. One is the hierarchical BoF-SIFT feature which can simultaneously describe local and global distributions. Another is the improved shape context feature which is used to describe the target edge contour point information. The other one is Hu moment invariants which gives better levels of recognition performance for large targets. Secondly, the recognition probabilities of these features are obtained using a one versus one support vector machine based on a radial basis function. Thirdly a strategy for multi-feature decision level fusion is designed. A large number of experiments show that the algorithm for multi-target classification of optical remote sensing images performs better with the recognition rate of targets reaching 93.52%.
References:
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